



'''simple docstring'''

import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


SCREAMING_SNAKE_CASE__     :						Optional[int]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Dict	=  list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
SCREAMING_SNAKE_CASE__     :						Optional[int]	=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 							a__:
			a_	: str									=      field(
			    default=snake_case__   ,      metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(snake_case__     )}     )
			a_	: str									=      field(
			    default=snake_case__   ,      metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''}     )
			a_	: int									=      field(
			    default=1_2_8   ,      metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,      )
			a_	: int									=      field(
			    default=1_2_8   ,      metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}   ,      )
			a_	: int									=      field(
			    default=6_4   ,      metadata={
			        '''help''': (
			            '''The maximum number of tokens for the question. Questions longer than this will '''
			            '''be truncated to this length.'''
			        )
			    }   ,      )
			a_	: int									=      field(
			    default=3_0   ,      metadata={
			        '''help''': (
			            '''The maximum length of an answer that can be generated. This is needed because the start '''
			            '''and end predictions are not conditioned on one another.'''
			        )
			    }   ,      )
			a_	: bool									=      field(
			    default=snake_case__   ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )
			a_	: bool									=      field(
			    default=snake_case__   ,      metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}     )
			a_	: float									=      field(
			    default=0.0   ,      metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}     )
			a_	: int									=      field(
			    default=2_0   ,      metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}     )
			a_	: int									=      field(
			    default=0   ,      metadata={
			        '''help''': (
			            '''language id of input for language-specific xlm models (see'''
			            ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
			        )
			    }   ,      )
			a_	: int									=      field(default=1   ,      metadata={'''help''': '''multiple threads for converting example to features'''}     )





class 							a__( snake_case__     ):
			a_	: Optional[int]									=      '''train'''
			a_	: Optional[int]									=      '''dev'''





class 							a__( snake_case__     ):
			a_	: SquadDataTrainingArguments
			a_	: List[SquadFeatures]
			a_	: Split
			a_	: bool






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = Split.train					,   _UpperCAmelCase = False					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "pt"					,   )							->							Any:
					snake_case__	     =args
					snake_case__	     =is_language_sensitive
					snake_case__	     =SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
					if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
							try:
									snake_case__	     =Split[mode]
							except KeyError:
									raise KeyError('mode is not a valid split name'						)
					snake_case__	     =mode
					# Load data features from cache or dataset file
					snake_case__	     ='v2' if args.version_2_with_negative else 'v1'
					snake_case__	     =os.path.join(
					    cache_dir if cache_dir is not None else args.data_dir					,   f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""					,   )

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					snake_case__	     =cached_features_file + '.lock'
					with FileLock(_UpperCAmelCase						):
							if os.path.exists(_UpperCAmelCase						) and not args.overwrite_cache:
									snake_case__	     =time.time()
									snake_case__	     =torch.load(_UpperCAmelCase						)

									# Legacy cache files have only features, while new cache files
									# will have dataset and examples also.
									snake_case__	     =self.old_features['features']
									snake_case__	     =self.old_features.get('dataset'					,   _UpperCAmelCase						)
									snake_case__	     =self.old_features.get('examples'					,   _UpperCAmelCase						)
									logger.info(
									    f"""Loading features from cached file {cached_features_file} [took %.3f s]"""					,   time.time() - start						)

									if self.dataset is None or self.examples is None:
											logger.warning(
											    f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
											    ' future run'						)
							else:
									if mode == Split.dev:
											snake_case__	     =self.processor.get_dev_examples(args.data_dir						)
									else:
											snake_case__	     =self.processor.get_train_examples(args.data_dir						)

									snake_case__     ,	snake_case__	     =squad_convert_examples_to_features(
									    examples=self.examples					,   tokenizer=_UpperCAmelCase					,   max_seq_length=args.max_seq_length					,   doc_stride=args.doc_stride					,   max_query_length=args.max_query_length					,   is_training=mode == Split.train					,   threads=args.threads					,   return_dataset=_UpperCAmelCase					,   )

									snake_case__	     =time.time()
									torch.save(
									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}					,   _UpperCAmelCase					,   )
									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
									logger.info(
									    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""						)






			def __len__(     self						)							->							Optional[int]:
					return len(self.features						)






			def __getitem__(     self					,   _UpperCAmelCase						)							->							Dict[str, torch.Tensor]:
					# Convert to Tensors and build dataset
					snake_case__	     =self.features[i]

					snake_case__	     =torch.tensor(feature.input_ids					,   dtype=torch.long						)
					snake_case__	     =torch.tensor(feature.attention_mask					,   dtype=torch.long						)
					snake_case__	     =torch.tensor(feature.token_type_ids					,   dtype=torch.long						)
					snake_case__	     =torch.tensor(feature.cls_index					,   dtype=torch.long						)
					snake_case__	     =torch.tensor(feature.p_mask					,   dtype=torch.float						)
					snake_case__	     =torch.tensor(feature.is_impossible					,   dtype=torch.float						)

					snake_case__	     ={
					    'input_ids': input_ids,
					    'attention_mask': attention_mask,
					    'token_type_ids': token_type_ids,
					}

					if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
							del inputs["token_type_ids"]

					if self.args.model_type in ["xlnet", "xlm"]:
							inputs.update({'cls_index': cls_index, 'p_mask': p_mask}						)
							if self.args.version_2_with_negative:
									inputs.update({'is_impossible': is_impossible}						)
							if self.is_language_sensitive:
									inputs.update({'langs': (torch.ones(input_ids.shape					,   dtype=torch.intaa						) * self.args.lang_id)}						)

					if self.mode == Split.train:
							snake_case__	     =torch.tensor(feature.start_position					,   dtype=torch.long						)
							snake_case__	     =torch.tensor(feature.end_position					,   dtype=torch.long						)
							inputs.update({'start_positions': start_positions, 'end_positions': end_positions}						)

					return inputs









'''simple docstring'''

import collections
import os
import re
from pathlib import Path


SCREAMING_SNAKE_CASE__     :						List[Any]	=  '''src/transformers'''


# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__     :						List[str]	=  re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE__     :						List[str]	=  re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE__     :						List[str]	=  re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE__     :						Tuple	=  re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
SCREAMING_SNAKE_CASE__     :						Any	=  re.compile(r'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
SCREAMING_SNAKE_CASE__     :						List[Any]	=  re.compile(r'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE__     :						Optional[int]	=  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
SCREAMING_SNAKE_CASE__     :						str	=  re.compile(r'''^\s*try:''')
# Catches a line with else:
SCREAMING_SNAKE_CASE__     :						Any	=  re.compile(r'''^\s*else:''')






def 				a  (     UpperCamelCase_    :    Dict			)       ->	List[str]:
		if _re_test_backend.search(UpperCamelCase_			) is None:
				return None
		snake_case__	     =[b[0] for b in _re_backend.findall(UpperCamelCase_			)]
		backends.sort()
		return "_and_".join(UpperCamelCase_			)






def 				a  (     UpperCamelCase_    :    List[Any]			)       ->	Tuple:
		with open(UpperCamelCase_      ,					'r'      ,					encoding='utf-8'      ,					newline='\n'			) as f:
				snake_case__	     =f.readlines()

		snake_case__	     =0
		while line_index < len(UpperCamelCase_			) and not lines[line_index].startswith('_import_structure = {'			):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(UpperCamelCase_			):
				return None

		# First grab the objects without a specific backend in _import_structure
		snake_case__	     =[]
		while not lines[line_index].startswith('if TYPE_CHECKING'			) and find_backend(lines[line_index]			) is None:
				snake_case__	     =lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(UpperCamelCase_			):
						snake_case__	     =_re_one_line_import_struct.search(UpperCamelCase_			).groups()[0]
						snake_case__	     =re.findall(r'\[([^\]]+)\]'      ,					UpperCamelCase_			)
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(', '			)]			)
						line_index += 1
						continue
				snake_case__	     =_re_import_struct_key_value.search(UpperCamelCase_			)
				if single_line_import_search is not None:
						snake_case__	     =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '			) if len(UpperCamelCase_			) > 0]
						objects.extend(UpperCamelCase_			)
				elif line.startswith(' ' * 8 + '"'			):
						objects.append(line[9:-3]			)
				line_index += 1

		snake_case__	     ={'none': objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith('if TYPE_CHECKING'			):
				# If the line is an if not is_backend_available, we grab all objects associated.
				snake_case__	     =find_backend(lines[line_index]			)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]			) is None:
						snake_case__	     =None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]			) is None:
								line_index += 1

						line_index += 1

						snake_case__	     =[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(' ' * 4			):
								snake_case__	     =lines[line_index]
								if _re_import_struct_add_one.search(UpperCamelCase_			) is not None:
										objects.append(_re_import_struct_add_one.search(UpperCamelCase_			).groups()[0]			)
								elif _re_import_struct_add_many.search(UpperCamelCase_			) is not None:
										snake_case__	     =_re_import_struct_add_many.search(UpperCamelCase_			).groups()[0].split(', '			)
										snake_case__	     =[obj[1:-1] for obj in imports if len(UpperCamelCase_			) > 0]
										objects.extend(UpperCamelCase_			)
								elif _re_between_brackets.search(UpperCamelCase_			) is not None:
										snake_case__	     =_re_between_brackets.search(UpperCamelCase_			).groups()[0].split(', '			)
										snake_case__	     =[obj[1:-1] for obj in imports if len(UpperCamelCase_			) > 0]
										objects.extend(UpperCamelCase_			)
								elif _re_quote_object.search(UpperCamelCase_			) is not None:
										objects.append(_re_quote_object.search(UpperCamelCase_			).groups()[0]			)
								elif line.startswith(' ' * 8 + '"'			):
										objects.append(line[9:-3]			)
								elif line.startswith(' ' * 12 + '"'			):
										objects.append(line[13:-3]			)
								line_index += 1

						snake_case__	     =objects
				else:
						line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		snake_case__	     =[]
		while (
		    line_index < len(UpperCamelCase_			)
		    and find_backend(lines[line_index]			) is None
		    and not lines[line_index].startswith('else'			)
		):
				snake_case__	     =lines[line_index]
				snake_case__	     =_re_import.search(UpperCamelCase_			)
				if single_line_import_search is not None:
						objects.extend(single_line_import_search.groups()[0].split(', '			)			)
				elif line.startswith(' ' * 8			):
						objects.append(line[8:-2]			)
				line_index += 1

		snake_case__	     ={'none': objects}
		# Let's continue with backend-specific objects
		while line_index < len(UpperCamelCase_			):
				# If the line is an if is_backend_available, we grab all objects associated.
				snake_case__	     =find_backend(lines[line_index]			)
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]			) is None:
						snake_case__	     =None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]			) is None:
								line_index += 1

						line_index += 1

						snake_case__	     =[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]			) <= 1 or lines[line_index].startswith(' ' * 8			):
								snake_case__	     =lines[line_index]
								snake_case__	     =_re_import.search(UpperCamelCase_			)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(', '			)			)
								elif line.startswith(' ' * 12			):
										objects.append(line[12:-2]			)
								line_index += 1

						snake_case__	     =objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects






def 				a  (     UpperCamelCase_    :    int      ,					UpperCamelCase_    :    List[Any]			)       ->	Union[str, Any]:

		def find_duplicates(UpperCamelCase_    :    Tuple			):
				return [k for k, v in collections.Counter(UpperCamelCase_			).items() if v > 1]

		if list(import_dict_objects.keys()			) != list(type_hint_objects.keys()			):
				return ["Both sides of the init do not have the same backends!"]

		snake_case__	     =[]
		for key in import_dict_objects.keys():
				snake_case__	     =find_duplicates(import_dict_objects[key]			)
				if duplicate_imports:
						errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}"""			)
				snake_case__	     =find_duplicates(type_hint_objects[key]			)
				if duplicate_type_hints:
						errors.append(f"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""			)

				if sorted(set(import_dict_objects[key]			)			) != sorted(set(type_hint_objects[key]			)			):
						snake_case__	     ='base imports' if key == 'none' else f"""{key} backend"""
						errors.append(f"""Differences for {name}:"""			)
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(f"""  {a} in TYPE_HINT but not in _import_structure."""			)
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(f"""  {a} in _import_structure but not in TYPE_HINT."""			)
		return errors






def 				a  (     )       ->	Optional[Any]:
		snake_case__	     =[]
		for root, _, files in os.walk(UpperCamelCase_			):
				if "__init__.py" in files:
						snake_case__	     =os.path.join(UpperCamelCase_      ,					'__init__.py'			)
						snake_case__	     =parse_init(UpperCamelCase_			)
						if objects is not None:
								snake_case__	     =analyze_results(*UpperCamelCase_			)
								if len(UpperCamelCase_			) > 0:
										snake_case__	     =f"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
										failures.append('\n'.join(UpperCamelCase_			)			)
		if len(UpperCamelCase_			) > 0:
				raise ValueError('\n\n'.join(UpperCamelCase_			)			)






def 				a  (     )       ->	Dict:
		snake_case__	     =[]
		for path, directories, files in os.walk(UpperCamelCase_			):
				for folder in directories:
						# Ignore private modules
						if folder.startswith('_'			):
								directories.remove(UpperCamelCase_			)
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(UpperCamelCase_			) / folder).glob('*.py'			)			)			) == 0:
								continue
						snake_case__	     =str((Path(UpperCamelCase_			) / folder).relative_to(UpperCamelCase_			)			)
						snake_case__	     =short_path.replace(os.path.sep      ,					'.'			)
						submodules.append(UpperCamelCase_			)
				for fname in files:
						if fname == "__init__.py":
								continue
						snake_case__	     =str((Path(UpperCamelCase_			) / fname).relative_to(UpperCamelCase_			)			)
						snake_case__	     =short_path.replace('.py'      ,					''			).replace(os.path.sep      ,					'.'			)
						if len(submodule.split('.'			)			) == 1:
								submodules.append(UpperCamelCase_			)
		return submodules


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  [
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]






def 				a  (     )       ->	Union[str, Any]:
		# This is to make sure the transformers module imported is the one in the repo.
		from transformers.utils import direct_transformers_import

		snake_case__	     =direct_transformers_import(UpperCamelCase_			)

		snake_case__	     =set(transformers._import_structure.keys()			)
		# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
		# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
		# (potentiall re-) add them.
		with open(os.path.join(UpperCamelCase_      ,					'__init__.py'			)      ,					'r'			) as f:
				snake_case__	     =f.read()
		import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]'      ,					UpperCamelCase_			)			)			)

		snake_case__	     =[
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
		]

		if len(UpperCamelCase_			) > 0:
				snake_case__	     ='\n'.join(f"""- {module}""" for module in module_not_registered			)
				raise ValueError(
				    'The following submodules are not properly registed in the main init of Transformers:\n'
				    f"""{list_of_modules}\n"""
				    'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'			)


if __name__ == "__main__":
			check_all_inits()
			check_submodules()




